US20110066065A1 - Systems and methods to identify a subgroup of adhd at higher risk for complicating conditions - Google Patents

Abstract

This invention is directed to embodiments of systems and methods to identify a subgroup of ADHD at higher risk for complicating conditions that, for example, may be of concern to an ADHD evaluation, may account for attention and behavior symptoms, and may lead a clinician to exclusion of ADHD from primary diagnosis. In one embodiment, a method for identifying a subgroup of attention deficit hyperactivity disorder (ADHD) patients at higher risk for complicating conditions is provided. The method can include receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis; obtaining EEG data for each of the plurality of patients; determining, based at least in part on the EEG data, an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation; correlating the indicator with a user's or clinician's evaluation data; and based at least in part on the correlation, determining at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.

Description

RELATED APPLICATION
• This application claims priority to U.S. Ser. No. 61/237,911, entitled “Systems and Methods to Identify a Subgroup of ADHD at Higher Risk for Complicating Conditions,” filed Aug. 28, 2009, the contents of which are hereby incorporated by reference.
FIELD OF THE INVENTION
• The invention relates to detection of biological disorders, and relates more particularly to systems and methods to identify a subgroup of ADHD at higher risk for complicating conditions.
BACKGROUND OF THE INVENTION
• The American Psychiatric Association (APA) publishes the Diagnostic and Statistical Manual of Mental Disorders (the current version is the DSM-IV-Text Revision). The DSM-IV provides definitions and criteria that support a clinician's diagnosis of mental disorders. The DSM-IV provides the generally accepted criteria for defining ADHD (attention-deficit/hyperactivity disorder). ADHD is characterized by DSM-IV criteria in part by presence of certain attention and behavior symptoms. Additionally, the DSM-IV criteria require that the symptoms are not better accounted for by another disorder. As such, ADHD evaluation can be relatively complicated because ADHD-like symptoms are known to be present in other psychiatric disorders, as well as in medical and neurological conditions. For instance, the DSM-IV criteria for depression include psychomotor agitation/restlessness, difficulty making decisions, and difficulty concentrating. The DSM-IV criteria for mania include excessive talking, racing thoughts, distractibility, and psychomotor agitation/restlessness. The DSM-IV criteria for anxiety disorder include concentration difficulties. There are also a number of medical mimics of ADHD including anemia, hearing or vision loss, congenital brain anomalies, mental retardation, medication effects, and head injury (Pearl et al., 2001). Thus, a need exists for an ADHD assessment aid that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
• ADHD behavior rating scales are assessment tools that are recommended in the support of ADHD diagnosis in professional guidelines such as those from the American Academy of Pediatrics (AAP, 2000). The rating scales are designed to assist in the recognition of the attention and behavior symptoms of ADHD as defined by the DSM-IV. However, it is commonly known that attention and behavior symptoms of ADHD are present with other disorders as well as medical and neurological conditions. In addition, many of these common disorders are comorbid in up to 67% of ADHD cases (Cantwell, 1996; Wolraich et al., 1998). Therefore in an ADHD diagnostic evaluation, a clinician faces the challenge of determining whether ADHD is the primary cause of the symptoms, whether the ADHD symptoms are secondary to other diagnoses, or whether ADHD is comorbid with other diagnoses (APA, 1994; Cantwell, 1996; Zametkin and Ernst, 1999). Therefore, a need still exists for an ADHD assessment aid that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
• Professional guidelines have been developed to assist in the implementation of DSM-IV criteria in ADHD diagnosis using evidence such as that from assessment tools. EEG (electroencephalography) is not currently listed in the DSM-IV criteria for ADHD. There is no known mention of using EEG with any guidelines that support the implementation of DSM-IV criteria in ADHD diagnosis such as the guidelines from the American Academy of Pediatrics (AAP, 2000).
• At least one conventional technique uses EEG data to diagnose ADHD using an assessment of EEG data collected from a patient. One conventional technique determines a ratio between digitized EEG data in the theta (4-8 Hz) and beta (13-21 Hz) frequency ranges collected from a patient. EEG data is collected from the patient during baseline and at least three attentive behavior tasks. An overall index is calculated for the patient using the ratios determined during the baseline and at least three attentive behavior tasks. The overall index is finally compared to a non-clinical database of normal individuals without ADHD or any neurological disorder to determine the presence and severity of the patient's ADHD. However, the conventional technique is limited to diagnosing ADHD in a patient, and does not address differentiating among ADHD patients diagnosed by a clinician. Furthermore, the conventional technique is a stand-alone diagnostic that does not support the implementation of DSM-IV criteria. Furthermore, the conventional technique does not address the DSM-IV criterion that requires that attention and behavior symptoms in a patient are not better accounted for by another condition. Finally, the conventional technique does not address whether attention and behavior symptoms in a patient are more likely to be due to ADHD or due to another condition. Therefore, a need still exists for an ADHD assessment aid that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
• At least one conventional technique uses EEG data to diagnose disorders of impaired attention using an assessment of EEG data collected from a patient. One conventional technique determines differences between EEG parameters collected from a patient during at least two tasks. An overall index is calculated for the patient using the absolute value of the sum of the EEG differences between tasks by distance. The overall index is finally compared to a non-clinical database of normal individuals without psychiatric disorders to determine the presence of a disorder with impaired attention such as ADHD. However, the conventional technique is limited to diagnosing attention disorders in a patient, and does not address differentiating among ADHD patients diagnosed by a clinician. Furthermore, the conventional technique does not address the DSM-IV criterion that requires that attention and behavior symptoms in a patient are not better accounted for by another condition. Finally, the conventional technique does not address whether attention and behavior symptoms in a patient are more likely to be due to ADHD or due to another condition. Therefore, a need still exists for an ADHD assessment aid that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
SUMMARY OF THE INVENTION
• Certain embodiments of the invention can use systems and methods to identify a subgroup of attention deficit hyperactivity disorder (ADHD) patients at higher risk for complicating conditions that, for example, may be of concern to an ADHD evaluation, may account for attention and behavior symptoms, and may lead a clinician to exclusion of ADHD from primary diagnosis. In one embodiment, EEG data is used to separate patients already diagnosed with ADHD into at least two subgroups having clinical differences that are relevant to a clinician's ADHD evaluation. In certain embodiments, one or more predefined cutoffs or thresholds can be applied to the EEG data to determine at least two subgroups having clinical differences that are relevant to a clinician's ADHD evaluation. In certain embodiments, a clinical database of patients with attention and behavior symptoms but not all with ADHD can be applied to the EEG data to determine at least two subgroups having clinical differences that are relevant to a clinician's ADHD evaluation. In certain other embodiments, using EEG data recorded under simple baseline conditions for each of a plurality of patients, the EEG data can be used to determine at least two subgroups having clinical differences that are relevant to a clinician's evaluation. In certain embodiments, EEG data is used to separate patients already diagnosed with ADHD into at least two subgroups with clinical differences that support addressing the DSM-IV criterion that requires that attention and behavior symptoms in a patient are not better accounted for by another condition. In certain embodiments, EEG data is used to separate patients already diagnosed with ADHD into at least two subgroups with clinical differences that support determining whether attention and behavior symptoms in a patient are more likely to be due to ADHD or due to another condition. In certain embodiments, EEG data is used to separate patients already diagnosed with ADHD into at least two subgroups with clinical differences that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
• In one embodiment, a system for identifying a subgroup of ADHD patients at higher risk for complicating conditions can be provided. The system can include a data collection module operable to receive, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis. The data collection module can be further operable to obtain EEG data for each of the plurality of patients. The system can further include a clinical/diagnostic module operable to determine, based at least in part on the EEG data, an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation. The system can also include a subgroup identification module operable to correlate the indicator with the user's or clinician's evaluation data, and to determine at least one subgroup of the plurality of patients based at least in part on the correlation, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.
• In another embodiment, a computer program product is provided. The computer program product can include a computer readable medium having computer readable code adapted to be executed to implement a method for identifying a subgroup of ADHD patients at higher risk for complicating conditions. The method can include providing a system, wherein the system comprises a plurality of software modules, wherein the plurality of software modules comprises a data collection module, a clinical/diagnostic module, a subgroup identification module, and optionally a report module. The method can further include receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis. The method can also include obtaining, via the data collection module, EEG data for each of the plurality of patients. The method can further include determining, based at least in part on the EEG data, an indicator of ADHD, wherein the EEG data supports a positive or negative ADHD evaluation. Further, the method can include determining, via the subgroup identification module, at least one subgroup of the plurality of patients based at least in part on the correlation, wherein the correlation provides positive or negative support for diagnosis of ADHD in at least one patient within the at least one subgroup.
• In yet another embodiment, a method for identifying a subgroup of ADHD patients at risk for complicating conditions is provided. The method can include receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis; obtaining EEG data for each of the plurality of patients; based at least in part on the EEG data, determining an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation; and based at least in part on the correlation, determining at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.
• Certain embodiments of the invention can use standardized EEG results to separate a previously diagnosed ADHD group into at least two subgroups, (1) a subgroup receiving confirmatory support, “CS”, and (2) a subgroup receiving a recommendation for further testing, “FT”. Certain embodiments of the invention can also facilitate increased clinical value because the FT and CS subgroups have significant differences in clinical characteristics that are meaningful to a user or clinician's evaluation. Furthermore, certain embodiments of the invention can use EEG results to separate a group of ADHD patients into clinically meaningful subgroups.
• Certain embodiments of the invention can support the integration of EEG into a clinical setting, to detect a subgroup of ADHD that is at higher risk for conditions that would be of concern to an ADHD evaluation, to provide recommendation for further testing in a subgroup of ADHD that is more likely to have complicating conditions, to detect a subgroup of ADHD that is at lower risk for conditions that would be of concern to an ADHD evaluation, and to provide confirmatory support of a user's or clinician's ADHD evaluation, or other ADHD evaluation in a subgroup of ADHD that is less likely to have complicating conditions.
• Other embodiments, aspects, and features of the invention will become apparent from the following detailed description, the accompanying drawings, and the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
• The foregoing, and other embodiments, aspects, and feature will be better understood from the following detailed description of the certain embodiments of the invention with reference to the drawings, in which:
• FIG. 1 illustrates an example environment and system in accordance with an embodiment of the invention.
• FIG. 2 illustrates an example summary for a particular patient's EEG results in accordance with an embodiment of the invention.
• FIG. 3 illustrates example interpretations for correlating an ADHD evaluation with a system result in accordance with an embodiment of the invention.
• FIG. 4 illustrates example interpretations when correlating an ADHD evaluation with a system result in accordance with an embodiment of the invention.
• FIG. 5 illustrates an example method in accordance with an embodiment of the invention.
• FIG. 6 illustrates example interpretation guidelines in accordance with an embodiment of the invention.
• FIGS. 7-10 illustrate various statistical clinical support in the form of accuracy results for an example implementation of an embodiment of the invention.
• FIGS. 11-20 illustrate various statistical clinical support in the form of odds ratio plots for an example implementation of an embodiment of the invention.
DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS
• Illustrative embodiments of the invention now will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout.
• Certain embodiments of the invention can use systems and methods to identify a subgroup of ADHD at higher risk for complicating conditions that, for example, would be of concern to an ADHD evaluation, may account for attention and behavior symptoms, and may lead a clinician to exclusion of ADHD from primary diagnosis. Certain embodiments of the invention can use EEG analysis and interpretation to separate a previously diagnosed ADHD group into, for instance, at least two clinically meaningful subgroups: (1) a subgroup receiving confirmatory support for a clinician's ADHD evaluation, and (2) a subgroup receiving a recommendation for further clinical testing of other conditions before proceeding with ADHD as primary diagnosis. Certain study results support the observation that there can be significant clinical differences in these two subgroups that would be of concern to a clinician conducting an ADHD evaluation. The clinical differences between subgroups can include risk of complicating conditions that could account for ADHD symptoms. The subgroup receiving confirmatory support is less likely to have complicating conditions that could account for ADHD symptoms. The subgroup receiving a recommendation for further clinical testing is more likely to have complicating conditions that could account for ADHD symptoms. Therefore, one technical effect of certain embodiments of the invention is to separate a previously diagnosed ADHD group into at least two clinically meaningful subgroups, which solves the prior technical problem of being unable to distinguish among patients in a previously diagnosed ADHD group, and addresses the need for an ADHD assessment aid that can effectively or otherwise suitably support separation of attention and behavior symptoms due to ADHD from those due to other conditions.
• FIG. 1 illustrates an example environment andsystem100 in accordance with an embodiment of the invention. Using asystem100 illustrated inFIG. 1, various methods for identifying a subgroup of ADHD patients at higher risk for complicating conditions in accordance with embodiments of the invention can be performed. In one example, the processes embodied inFIGS. 3,4 and5 can be implemented with thesystem100 shown inFIG. 1. Other systems in accordance with other embodiments of the invention can include similar system components as shown inFIG. 1, or other components, elements, and modules.
• In the example shown inFIG. 1, the environment can be a client-server configuration, and thesystem100 can be a subgroup identification system. Thesystem100 is shown with acommunications network102, such as the Internet, in communication with at least oneclient device104A. Any number ofother client devices104N can also be in communication with thenetwork102. In any instance, each of theclient devices104A-104N can be operable to receive information from at least one patient or user. Thenetwork102 is also shown in communication with at least oneserver106, such as a website host server. Any number of other servers or website host servers can also be in communication with thenetwork102.
• Thecommunications network102 shown inFIG. 1 can be, for example, the Internet. In another embodiment, thenetwork102 can be a wireless communications network capable of transmitting both voice and data signals, including image data signals or multimedia signals. Other types of communications networks, including local area networks (LAN), wide area networks (WAN), a public switched telephone network, or combinations thereof can be used in accordance with various embodiments of the invention.
• Eachclient device104A-104N can be a computer or processor-based device capable of communicating with thecommunications network102 via a signal, such as a wireless frequency signal or a direct wired communication signal. A respective communication or input/output interface120 associated with eachclient device104A-104N can facilitate communications between theclient device104A-104N and thenetwork102 or Internet. Each client device, such as104A, can include aprocessor108 and a computer-readable medium, such as a random access memory (RAM)110, coupled to theprocessor108. Theprocessor108 can execute computer-executable program instructions stored in memory110. In some embodiments, the computer executable program instructions stored in memory110 can include an Internet browser application program. The Internet browser application program can be adapted to access and/or receive one or more webpages and associated content from at least one remotely located server, such as106, or website host server. The computer executable program instructions stored in memory110 can also include a data collection module or application program, such as112; a clinical/diagnostic module or application program, such as114; a subgroup identification module or application program, such as116; and optionally a report module or application program, such as118. Thedata collection module112 can be adapted to access and/or receive EEG or biological data from a biological data collector or EEG collection device in communication with a patient, or a from a client device, such as104A, via a network, such as102. EEG or biological data can be received in realtime or near-realtime, or may be previously stored from a prior data collection session. Thedata collection module112 can further be adapted to receive, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis. In certain embodiments, thedata collection module112 also can be adapted to identify a plurality of patients having ADHD as a primary diagnosis. For example, using an online questionnaire or series of questions designed to elicit patient information associated with diagnosing ADHD in the patient, a data collection module such as112 can receive responses from the patient and/or other suitable informant(s). Based at least in part on the responses from the patient and/or other suitable informant(s), thedata collection module112 can identify a plurality of patients having ADHD as a primary diagnosis.
• The clinical/diagnostic module114 can be adapted to, based at least in part on the EEG or biological data, determine an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation.
• Thesubgroup identification module116 can be adapted to correlate the indicator with the user's or clinician's evaluation data. Further, thesubgroup identification module116 can be adapted to, based at least in part on the correlation, determine at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.
• Theoptional report module118 can be adapted to output an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.
• In one embodiment, the computer executable program instructions stored in memory110 and associated functionality facilitated bymodules112,114,116, and118 can be implemented by a NEBA™ (neuropsychiatric EEG-based assessment aid for ADHD) system, provided by Lexicor Medical Technology, Inc.
• Theserver106 can be a computer or processor-based device capable of communicating with thecommunications network102 via a signal, such as a wireless frequency signal or a direct wired communication signal.Server106, depicted as a single computer system, may be implemented as a network of computer processors. Examples of suitable servers are server devices, mainframe computers, networked computers, a processor-based device, and similar types of systems and devices. The server, such as106, can include a processor126 and a computer-readable medium, such as a random access memory (RAM)128, coupled to the processor126. The processor124 can execute computer-executable program instructions stored inmemory128. In some embodiments, the computer executable program instructions stored inmemory128 can include a website server application program. The website server application program can be adapted to transmit one or more webpages and any associated content from theserver106. Similar to the computer executable program instructions above in theclient devices104A-104N, the computer executable program instructions stored inmemory128 can also include a data collection module or application program, such as130; a clinical/diagnostic module or application program, such as132; a subgroup identification module or application program, such as134; and optionally a report module or application program, such as136. Thedata collection module130 can be adapted to access and/or receive EEG or biological data from a biological data collector or EEG collection device in communication with a patient, or from a client device, such as104A, via a network, such as102. EEG or biological data can be received in realtime or near-realtime, or may be previously stored from a prior data collection session. Thedata collection module130 can further be adapted to receive, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis. In certain embodiments, thedata collection module130 can be adapted to identify a plurality of patients having ADHD as a primary diagnosis. For example, using an online questionnaire or series of questions designed to elicit patient information associated with diagnosing ADHD in the patient, a data collection module such as130 can receive responses from the patient and/or other suitable informant(s). Based at least in part on the responses from the patient and/or suitable informant(s), thedata collection module130 can identify a plurality of patients having ADHD as a primary diagnosis. The clinical/diagnostic module132 can be adapted to, based at least in part on the EEG or biological data, determine an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation. Thesubgroup identification module134 can be adapted to correlate the indicator with the user's or clinician's evaluation data. Further, thesubgroup identification module134 can be adapted to, based at least in part on the correlation, determine at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup. Theoptional report module136 can be adapted to output an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.
• A user, such as120, can be a person associated with a health care entity, or a researcher. In either instance, the user can operate a computer or processor-based device capable of communicating with thecommunications network102 via a signal, such as a wireless frequency signal or a direct wired communication signal. In at least one embodiment, more than one user, such as120, can be in communication with arespective client device104A-104N.
• A data collection module, such as112 and130, can be adapted to receive and/or collect various biological or EEG data from any number ofclient devices104A-104N, EEG data collection devices, and/orservers106. The data collection module, such as112 and130, can also be adapted to identify one or more patients having ADHD as a primary diagnosis. In one embodiment, a data collection module can include computer code operable to receive and/or collect various biological or EEG data from any number ofclient devices104A-104N, EEG data collection devices, and/orservers108. Furthermore, a data collection module can include computer code operable to identify one or more patients having ADHD as a primary diagnosis. Thedata collection module112 can include additional computer code operable to receive, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis.
• A clinical/diagnostic module, such as114 and132, can be adapted to receive and/or collect a clinical evaluation for one or more patients from at least one clinician or other health care professional. In one embodiment, a clinical/diagnostic module can include computer code operable to receive and/or collect a clinical evaluation for one or more patients from at least one clinician or other health care professional. In another instance, a clinical/diagnostic module can be adapted to support, based at least in part on received EEG or biological data, at least one clinical evaluation for one or more patients. In one embodiment, a clinical/diagnostic module can include computer code operable to support, based at least in part on received EEG or biological data, at least one clinical evaluation for one or more patients. In any instance, a clinical/diagnostic module can include computer code operable to, based at least in part on the EEG data, determine an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation.
• A subgroup identification module, such as116 and134, can be adapted to correlate the indicator with the user's or clinician's evaluation data. In one embodiment, a subgroup identification module can include computer code operable to correlate the indicator with the user's or clinician's evaluation data. The subgroup identification module can be further adapted to determine, based at least in part on the correlation, at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup. In one embodiment, a subgroup identification module can include computer code operable to determine, based at least in part on the correlation, at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.
• An optional report module, such as118 and136, can be adapted to output an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient. In one embodiment, a report module can include computer code operable to output an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.
• Generally, each of thememories110 and128, and data storage devices ordatabases124A-124N can store data and information for subsequent retrieval. In this manner, thesystem100 can store various received or collected EEG and/or biological data or patient files in memory or a database associated with a client device, such as104A, or a server, such as106. Thememories110 and128, and data storage devices ordatabases124A-124N can be in communication with each other and/or other databases, such as a centralized database, or other types of data storage devices. When needed, data or information stored in a memory or database may be transmitted to a centralized database capable of receiving data, information, or data records from more than one database or other data storage devices. In other embodiments, thedatabases124A-124N shown can be integrated or distributed into any number of databases or data storage devices.
• Suitable processors for aclient device104A-104N and aserver106 may comprise a microprocessor, an ASIC, and state machine. Example processors can be those provided by Intel Corporation and Motorola Corporation. Such processors comprise, or may be in communication with media, for example computer-readable media, which stores instructions that, when executed by the processor, cause the processor to perform the elements described herein. Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as theprocessors108 and126, with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.
• Client devices104A-104N may also comprise a number of other external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display, or other input or output devices. As shown inFIG. 1, a client device such as104A can be in communication with an output device via a communication or input/output interface, such as122. Examples ofclient devices104A-104N are personal computers, mobile computers, handheld portable computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, desktop computers, laptop computers, Internet appliances, and other processor-based devices. In general, a client device, such as104A, may be any type of processor-based platform that is connected to a network, such as102, and that interacts with one or more application programs.Client devices104A-104N may operate on any operating system capable of supporting a browser or browser-enabled application including, but not limited to, Microsoft Windows®, Apple OSX™, and Linux. Theclient devices104A-104N shown include, for example, personal computers executing a browser application program such as Microsoft Corporation's Internet Explorer™, Netscape Communication Corporation's Netscape Navigator™, and Apple's Safari™, and Mozilla Firefox™
• In one embodiment, suitable client devices can be standard desktop personal computers with Intel x86 processor architecture, operating a Microsoft® Windows® operating system, and programmed using a Java language.
• A user, such as120, can interact with a client device, such as104A, via any number of input and output devices (not shown) such as an output display device, keyboard, and/or a mouse. In this manner, the user, such as120, can access one or more webpages located on a website server host, such as106, via an Internet browser application program operating on a client device, such as104A. In the embodiment shown, auser120 can receive information associated with at least one ADHD patient via theclient device104A. In one example, theclient device104A can access or otherwise receive EEG or biological data associated with one or more patients. In another example, theclient device104A can access or otherwise receive user's or clinician's evaluations for patients identified with ADHD as a primary diagnosis. In yet another example, theclient device104A can receive one or more patient inputs to an online questionnaire or series of questions designed to elicit patient information associated with diagnosing ADHD in the patient. In any instance, theclient device104A can provide one or more options for theuser120 to request or otherwise retrieve certain information accessible by theclient device104A.
• After theclient device104A receives a user input, theclient device104A can, in certain instances, identify a plurality of patients having ADHD as a primary diagnosis. For example, theclient device104A can obtain previously stored EEG or biological data for each of the plurality of patients, wherein the EEG or biological data can be analyzed and/or evaluated to support a positive or negative ADHD evaluation. In another example, theclient device104A can collect some or all previously submitted clinical evaluations associated with a plurality of patients having ADHD as a primary diagnosis. In yet another example, theclient device104A can analyze and determine from one or more patient's and/or other suitable informants' responses to an online questionnaire or series of questions, a plurality of patients having ADHD as a primary diagnosis. In any instance, based at least in part on the EEG or biological data, theclient device104A can determine an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation.
• Theclient device104A can correlate the indicator with a user's or clinician's evaluation data, and based at least in part on the correlation, theclient device104A can determine at least one subgroup of the plurality of ADHD patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup. Theclient device104A can provide an output to theuser120, wherein theuser120 can view a report or other indication corresponding to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.
• Other system embodiments in accordance with the invention can include fewer or greater numbers of components and may incorporate some or all of the functionality described with respect to the system components shown inFIG. 1. One will appreciate that components of thesystem100 shown in and described with respect toFIG. 1 are provided by way of example only. Numerous other operating environments, system architectures, and device configurations are possible. Accordingly, embodiments of the invention should not be construed as being limited to any particular operating environment, system architecture, or device configuration.
• In one example, a data collection module similar to112 and130, clinical/diagnostic module similar to114 and132, subgroup identification module similar to116 and134, and report module similar to118 and136, according to an embodiment of the invention, can be implemented by the NEBA™ system, provided by Lexicor Medical Technology, Inc. In this embodiment, thesystem100 can support a 15-20 minute non-invasive office-based test developed to provide a relatively reliable, quantitative measurement of neural activity in the cortex at the electrode CZ (of theInternational 10/20 electrode placement system). The functionality of some or all of the modules112-118,130-136 shown inFIG. 1 can be performed by the NEBA™ system using some or all of the following high-level sub-system components: (1) CEEG (compact EEG) recording system, (2) EEG data archive and communications system (EDACS), and (3) NEBA™ analysis system. The high-level sub-system components can be used to compare an individual patient's quantified EEG with certain clinical reference values. Thesystem100 can provide a clinician or user with a specific EEG marker of activity, for example, in the form of a power ratio. In this example, a power ratio can be computed by dividing the average percent power in the theta frequency band (about 4-7.5 Hz) by the average percent power in the beta frequency band (about 13-20.5 Hz) and is termed the theta/beta ratio (TBR). TBR cutoffs provided are specific to the NEBA™ system processing and analysis of EEG, but can be adjusted as needed or desired in other system embodiments. TBR cutoffs specify categories of “positive,” “negative,” or “uncertain” for certain predefined or specified ranges or thresholds of TBR associated with ADHD.
• In one embodiment, TBR cutoffs can be determined for one or more ranges or thresholds and corresponding categories, for example, as follows: in at least one patient within a predefined or specified age range with ADHD identified as a primary diagnosis by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides positive support for diagnosis of ADHD, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD; in at least one patient with whom ADHD presence as a primary diagnosis is considered to be “uncertain” by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing with focus on ruling in/out ADHD before considering other disorders for primary diagnosis, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD; and in at least one patient with whom ADHD is determined to be absent or secondary to another condition by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within predefined or specified ranges provides a suggestion for reviewing other possibilities for an elevated theta/beta ratio (TBR), and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides confirmatory support for absence of ADHD.
• In one embodiment, TBR cutoffs can include one or more of the following ranges or thresholds and corresponding categories, for example, as follows: in at least one patient aged around 6.00 to 11.99 years old with ADHD identified as a primary diagnosis by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides positive support for diagnosis of ADHD, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD.
• In another embodiment, TBR cutoffs can include one or more of the following ranges or thresholds and corresponding categories, for example, as follows: in at least one patient aged around 12.00 to 17.99 years old with ADHD identified as a primary diagnosis by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides positive support for diagnosis of ADHD, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD.
• In one embodiment, TBR cutoffs can be determined for one or more ranges or thresholds and corresponding categories, for example, as follows: in at least one patient aged less than 6.00 years old with ADHD identified as a primary diagnosis by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides positive support for diagnosis of ADHD, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD.
• In one embodiment, TBR cutoffs can be determined for one or more ranges or thresholds and corresponding categories, for example, as follows: in at least one patient aged greater than 17.99 years old with ADHD identified as a primary diagnosis by a clinician or user, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides positive support for diagnosis of ADHD, EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and EEG data comprising a theta/beta ratio (TBR) calculation within a predefined or specified range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD.
• In other example embodiments, other TBR cutoffs can be determined for one or more predefined or specified ranges or thresholds and corresponding categories of patients.
• In one embodiment, a data collection module similar to112 and130, can collect biological data such as EEG data using noninvasive electrodes that are placed on a patient's head. For example, an operator can place a single recording electrode (CZ) on the patient's scalp with conductive gel to ensure a suitable connection for data collection. The electrode is placed in accordance with the International 10-20 system using an electrode headband with a ground electrode near FZ and linked ears reference. Electro-oculography (EOG) can be used to monitor eye blinks and gross eye movement. In certain instances, one or more trained technicians or users can perform manual (visual detection) and algorithm-based (50 μV threshold) artifacting of the EEG data using frequency spectrum analysis software. In any instance, EEG data can be processed with frequency spectrum analysis software and/or other techniques or routines to transform the EEG data into the frequency domain and perform calculations to determine the TBR. The EEG data and/or TBR for the patient of interest can be used to support an ADHD evaluation of the patient.
• In one instance, an output from a data collection module, such as112 and130, can be provided for a user or clinician, such as120. Output from the data collection module, such as112 and130, can include, for example, EEG results, cutoffs, and a clinical database comparison that can be presented in a report, such as shown inFIG. 2.
• In one example, a data collection module, such as112 and130, can present an indicator of ADHD, such as a patient's theta/beta ratio (TBR), to a clinician oruser120 as follows:
• “The result is (positive/negative/uncertain) for a significant increase in TBR, as processed by standardized NEBA™ methods and compared to the NEBA™ clinical database. Please refer to manual for further information supporting interpretation of TBR.”
• As shown inFIG. 2, in accordance with an embodiment of the invention, anexample output200 from a data collection module, such as112 or130, can provide a summary for a particular patient's EEG or other biological data results, including a measurement ofneural activity202, a quantitative analysis ofEEG data204, aclinical database comparison206, and an EEG data interpretation supporting anADHD evaluation208.
• In this embodiment, a data collection module, such as112 or128, can use the collected EEG data and/or TBR to support an ADHD evaluation of the patient. For example, after the data collection module collects or receives EEG or biological data for a patient of interest, the data collection module can determine a TBR for the patient. The data collection module can then compare the patient's TBR with a population sample having one or more similar characteristics to the patient's demographics, such as age. Based at least in part on the comparison, the data collection module can support an ADHD evaluation of the patient, such as positive or a negative evaluation, by providing confirmatory support for a user's or clinician's ADHD evaluation or providing a recommendation of further testing for complicating conditions that could be of concern to the user's or clinician's ADHD evaluation. As shown inFIG. 2, the collected EEG or biological data in202 can be monitored and stored by the data collection module. Aquantitative analysis204 by the data collection module can determine certain variables for the collected EEG or biological data, for instance, absolute theta power, absolute beta power, total power, relative theta power, relative beta power, and theta/beta ratio (TBR). As shown in206, using some or all of the variables from thequantitative analysis204, the data collection module can compare the patient's TBR with a population sample of one or more patients or other subjects having one or more similar characteristics to the patient's demographics. In this example, children aged 6.0 to 11.9 years old who presented attention and/or behavior problems were selected as a population sample. The example inFIG. 2 shows a pair offlat scatter plots210 and212, which respectively illustrate the TBRs of patients without ADHD and the TBRs of patients with ADHD. Athird plot214 below the pair ofscatter plots210,212 illustrates the TBR of the patient of interest. As shown inFIG. 2, thethird plot214 indicates a patient of interest's TBR to be about 6.74, which compared to the pair ofscatter plots210,212 is above the equivocal zones and within the ranges of TBR of significant increase relative to the population sample. As indicated by208, an output of “The result is positive for a significant increase in TBR, as processed by standardized NEBA™ methods and compared to the NEBA™ clinical database. Please refer to manual for further information supporting interpretation of TBR.” In any instance, based at least in part on the comparison of the patient of interest's TBR with those of the selected population of sample, an ADHD evaluation of the patient, such as positive or a negative evaluation, can be supported by the data collection module, by providing confirmatory support for a user's or clinician's ADHD evaluation or providing a recommendation of further testing for complicating conditions that could be of concern to the user's or clinician's ADHD evaluation.
• In one embodiment, to interpret a patient's case, a user such as120 or clinician first performs or otherwise receives a clinical evaluation for the patient of interest and determines whether ADHD is present as the primary diagnosis. A primary diagnosis may be defined as meaning that ADHD would be the condition managed and treated first by auser120 or clinician, and that other conditions may have been ruled out as a primary diagnosis. Theuser120 or clinician can then input the clinical evaluation to thesystem100, or alternatively, can refer to results from thesystem100. In certain instances, a set of guidelines, such as the NEBA™ Interpretation Guidelines shown inFIG. 4, can offer auser120 or clinician, for example, two general possibilities that depend on the agreement ofsystem100 with the evaluation: (1) Confirmatory support; or (2) Further clinical testing recommended.
• In another embodiment, automated interpretation of a patient's case could be performed by the client device, such as104A. For example, using an online questionnaire or series of questions designed to elicit patient information associated with diagnosing ADHD in the patient, the data collection module can receive responses from the patient and/or other suitable informant(s) to the questionnaire or series of questions. The questions can include, but are not limited to, “Are you generally restless or fidgety?”, “Do you have difficulty following instructions?”, and “Are you easily distracted?”, as well as “Is the child or adolescent generally restless or fidgety?”, “Does the child or adolescent have difficulty following instructions?”, and “Is the child or adolescent easily distracted?”. Based at least in part on the responses from the patient and/or suitable informant(s), the data collection module can provide an evaluation for the patient of interest and determine whether ADHD is present as the primary diagnosis. The automated evaluation can then be utilized in lieu of a user's or clinician's evaluation.
• FIG. 3 illustrates general example interpretations delineated by a correlation of a clinical ADHD evaluation with a system result in accordance with an embodiment of the invention. As shown inFIG. 3, a three by threegrid300 can be used to correlate an ADHD evaluation (negative, uncertain, positive) with the system result (negative, uncertain, positive). Thegrid300 and associated interpretations shown inFIG. 3 can be stored in a memory, such as110 and128, and/or one or more data storage devices or databases, such as124A-124N, wherein a subgroup identification module or application program, such as116 and134, can access thegrid300 and correlate an evaluation of ADHD with an ADHD indicator. Other grids, data structures, and combinations of evaluations and system results can be used to correlate an evaluation of ADHD with an ADHD indicator. In use, thegrid300 and associated interpretations correlate the result of the ADHD evaluation with the ADHD indicator or system result to obtain an interpretation. For example, a user or clinician's positive ADHD evaluation correlated with a positive system result equates to a “Confirmatory Support” interpretation in the upper right corner of thegrid300. By way of further example, a positive user or clinician's ADHD evaluation correlated with a negative system result equates to a “Strongly Recommend Further Clinical Testing” interpretation in the upper left corner of thegrid300. Equivocal (uncertain) zones are highlighted in grey in thegrid300.
• As shown by the embodiment ofFIG. 3, the absence of ADHD in a particular patient can be determined by the user or clinician after a clinical ADHD evaluation; that is, if ADHD DSM-IV criteria are not present, there can be no clinical diagnosis of ADHD. In certain embodiments, the absence of ADHD in a particular patient can be supported by a data collection module or application program, such as112 and130, operating with a clinical/diagnostic module or application program, such as114 and132. For example, data collection module or application program, such as112, and a clinical/diagnostic module or application program, such as114, can include computer code to receive input from a user or clinician, such as120, indicating a result of a clinical ADHD evaluation. In another example, a data collection module or application program, such as112 and130, operating in conjunction with a clinical/diagnostic module, such as114 and132, can include computer code to evaluate responses from a patient and/or other suitable informant to an online questionnaire or series of questions, and based at least in part on the responses from the patient and/or suitable informant, can provide an ADHD evaluation, which identifies one or more patients having ADHD as a primary diagnosis. In either instance, a subgroup identification module or application program, such as116 and134, can correlate the ADHD evaluation with the system result to obtain an interpretation.
• The example equivocal zone inFIG. 3 can offer a graded strength behind certain recommendations. For instance, if a system result is equivocal (uncertain), the example guidelines can “suggest” further clinical testing. If there is disagreement between the user or clinician's evaluation and a system negative result, the example guidelines can “strongly recommend” further clinical testing. When further clinical testing is recommended, suggestions can be provided for the initial focus as shown inFIG. 4.
• Example details of the NEBA™ Interpretation Guidelines are shown inFIG. 4 in accordance with an embodiment of the invention. In continuing the above example, for a user or clinician's positive ADHD evaluation correlated with a positive system result in thegrid400 ofFIG. 4, the full interpretation equates to a “Confirmatory support for presence of ADHD. Suggest using EEG as part of discussion with patient on biological aspects of disorders.” evaluation shown in the first horizontal line of thegrid400. Continuing the other example above, for a positive user or clinician's ADHD evaluation correlated with a negative system result, the full interpretation equates to a “Strongly recommend further clinical testing with focus on ruling in/out other conditions before proceeding with ADHD as primary diagnosis.” evaluation shown in the third horizontal line of thegrid400. Similar togrid300,grid400 and associated interpretations shown inFIG. 4 can be stored in a memory, such as110 and128, and/or one or more data storage devices or databases, such as124A-124N, wherein a subgroup identification module or application program, such as116 and134, can access thegrid400 and correlate an evaluation of ADHD with an ADHD indicator. Other grids, data structures, and combinations of evaluations and system results can be used to correlate an evaluation of ADHD with an ADHD indicator.
• In this embodiment, functionality provided by the system, such as the NEBA™ system orsystem100 inFIG. 1, can be one part of a psychiatric workup for ADHD. In certain embodiments, the functionality provided by the system can be used to support the evaluation or diagnosis of ADHD, and in other embodiments, the functionality of the system is not to be exclusively used in the evaluation or diagnosis of ADHD. In any instance, the functionality provided by the system can aid in the interpretation of a patient's TBR increase in the context of certain cutoffs, processing, standardization, and database comparison provided by the system. For example, the system can be used by a user or clinician as confirmatory support for a completed clinical evaluation or as support for the user or clinician's decision to pursue further testing following a clinical evaluation.
• In one embodiment, a system, such as the NEBA™ system orsystem100 inFIG. 1, can separate an ADHD group of patients into two subgroups: (1) a subgroup receiving confirmatory support for a completed clinical evaluation, and (2) a subgroup receiving support for the user or clinician's decision to pursue further testing following a clinical evaluation. An example separation of an ADHD group into subgroups by a process implemented by a system such as100 is illustrated inFIG. 5, which represents one example how a clinician or user such as120 could integrate thesystem100 or system similar to the NEBA™ system into a patient evaluation process.
• InFIG. 5, themethod500 begins atblock502, in which data for patients with attention and/or behavior concerns, such as ADHD, is received. For example, in some instances, data associated with one or more patients with attention and/or behavior concerns can be input to the system, such as100 inFIG. 1, by a clinician or user, such as120. In another example, data associated with one or more patients with attention and/or behavior concerns can be retrieved by a data collection module, such as112 inFIG. 1, from a data storage device, such as124A. In either instance, a data collection module or application program, such as112 or130, can receive the data associated with one or more patients.
• Block502 is followed byblock504, in which a diagnostic evaluation for each patient is received. For example, in some instances, a clinician or user, such as120, can perform a diagnostic or clinical ADHD evaluation per the standard of his or her practice, and the clinical evaluation can be submitted to a data collection module, such as112 inFIG. 1. In other instances, an evaluation can be supported using a clinical evaluation from a clinical/diagnostic module or application program, such as114 inFIG. 1.
• In any instance, based at least in part on the patients' clinical evaluations, the patients can initially be divided into at least two groups as shown inblocks506 and508. For example, based at least in part on the user's or clinician's evaluation, the patients can be separated into two groups: (1) Patients with ADHD (full threshold) as primary diagnosis inblock508, and (2) All other patients (ADHD is absent or secondary or sub-threshold) inblock506. At this point, the clinician or user such as120 may decide to apply certain functionality of the system, such as the NEBA™ system, to the patients who have ADHD as primary diagnosis, such as those determined inblock508.
• Block508 is followed byblock510, in which EEG or biological data from each of the patients who have ADHD as primary diagnosis is used to support a positive or negative ADHD evaluation. For example, as explained above with respect toFIG. 2, using a comparison of a patient's TBR (theta-beta ratio) with a predefined population sample, a clinical/diagnostic module or application program, such as114 and132, and subgroup identification module or application program, such as116 and134, can facilitate support of an ADHD evaluation. In this embodiment, based at least in part on the EEG data, the ADHD patients (primary diagnosis) can be separated into two groups as shown inblocks512 and514: (1) ADHD patients receiving confirmatory support inblock512, and (2) ADHD patients receiving a recommendation for further testing inblock514.
• Block514 is followed byblock516, in which an additional diagnostic evaluation for each patient receiving a recommendation for further testing is received, and based on the results of such testing a determination for each patient is further made. For example, in some instances, a clinician or user such as120 can decide whether to follow a recommendation for further testing fromblock514. The further testing would be performed by the clinician or user such as120 per the standard of his or her practice, or performed by a specialist through referral. The further testing could determine whether any complicating conditions are present which may be of concern to an ADHD evaluation and might lead to exclusion of ADHD from primary diagnosis for the patient of interest.
• In another embodiment, a subgroup identification module, such as116 and134, can receive additional diagnostic information from a database, such as124A-124N or a user or clinician, such as120. Based at least in part on the additional diagnostic information received, the subgroup identification module, such as116 and134, can support separation of the group of ADHD patients recommended for further testing into two further subgroups.
• In any instance, block518 represents the subgroup of patients in which further testing is performed confirming the patient with ADHD as previously diagnosed by clinician or user has complicating conditions that may be of concern to an ADHD evaluation and might exclude ADHD from a primary diagnosis, and block520 represents the subgroup of patients in which further testing is performed confirming the ADHD evaluation as the primary diagnosis and confirming that complicating condition are absent or present but do not lead to excluding ADHD from a primary diagnosis.
• Returning to the results of the additional testing, such testing can indicate the presence of possible complications or complicating conditions. Possible complications or complicating conditions include, but are not limited, to the following: (1) a psychiatric disorder that could lead to ADHD exclusion, such as (a) pervasive developmental disorders, psychotic disorders, bipolar disorders, and disorders caused by a stressing event, namely post-traumatic stress disorder and adjustment disorder; (2) a medical or neurological condition that could simulate ADHD, such as conditions involving head injury with ongoing problems, sensory integration dysfunction, auditory processing disorder, substance abuse, tobacco exposure, anemia, headaches affecting attention, congenital encephalopathy, cerebral palsy, mild mental retardation, neuromaturational delays/soft signs, and influence of asthma medications; (3) an uncorrected vision or hearing problem which could simulate ADHD; (4) history of adverse events and/or no improvement with ADHD medications, such as cardiac problems, tics, insomnia, weight loss, depression, compulsiveness, irritability, anxiety, withdrawal, lethargy, nausea, and headaches; (5) aggression and/or anger issues as a primary concern such as anger/aggression as primary concern and/or arising specifically with ADHD treatment, probable to definite conduct disorder (CD), and oppositional defiant disorder (ODD); (6) doing well academically and intellectually, such as reported in interview and/or questionnaire as doing well academically/intellectually, no special education, and no repeated grade; (7) dissatisfaction with a previous or current ADHD diagnosis, such as when a patient may have presented because further evaluation was wanted after previous ADHD diagnosis, or because evaluation was sought for other disorders not ADHD, or patient may have had general dissatisfaction with ADHD treatment, or parents and/or teachers may have both ruled out ADHD (all scale scores <80th percentile). A clinical/diagnostic module, such as114 and132, can receive this additional information for some or all of the ADHD patients fromblock514, and based at least in part on this additional information can separate the ADHD patients into at least two subgroups.
• Thus, inblock516, information from an additional diagnostic evaluation can be used to separate the ADHD patients fromblock514 into two subgroups inblocks518 and520.
• Afterblocks518 and520, themethod500 ends.
• Themethod500 can be implemented with thesystem100 shown inFIG. 1. Other embodiments of themethod500 can include fewer or greater numbers of elements, and may be implemented with other system embodiments in accordance with the invention.
• FIGS. 6-20 illustrate various supporting data for an example system and process implementation for at least one embodiment of the invention. In this embodiment, one system measure or indicator (EEG theta/beta ratio) can be reliably determined, and the intraclass correlation coefficient (ICC) of repeated system measures or indicators (test-retest reliability) was evaluated at about 0.83. In further evaluations, ICC was about 0.85 and about 0.88. To determine reliability, system EEG measurements were recorded on two separate visits from children and adolescents (n=198) aged 6.00 to 17.99 years who presented to 13 clinics with attention and behavior concerns. Three different EEG technicians separately performed artifact removal on system EEG data resulting in the three ICC results.
• In support of system diagnostic accuracy per the intended use, positive predictive value (PPV) was about 94%, negative predictive value (NPV) was about 80%, sensitivity was about 82%, specificity was about 93%, and overall accuracy was about 87%. To evaluate diagnostic accuracy per the intended use,FIG. 6 represents the true and false answers of the system. A true answer for a system result of confirmatory support “CS” is when a system positive for theta/beta ratio (TBR) being relatively high (EEG-yes) agrees with the clinician's positive diagnosis (ADHD-yes). A false answer for confirmatory support “CS” is when a system positive (EEG-yes) disagrees with the clinician's negative diagnosis (ADHD-no). Positive predictive value (PPV) represents the chance that when an individual patient receives a system positive, that the patient has ADHD as primary diagnosis. To evaluate the performance of the system result of recommendation for further testing “FT”, it was also necessary to determine whether clinically meaningful conditions were likely to be found. Conditions can be considered clinically meaningful to the user or clinician's diagnosis if the conditions may be of concern to an ADHD evaluation and might lead the clinician to exclude ADHD from primary diagnosis. Conditions or complications that were considered in the evaluation of system were those listed above. When evaluating per the intended use, a system negative (EEG-no; TBR is relatively low) for an ADHD patient results in a recommendation for further testing for other conditions (complications) that may be of concern to an ADHD evaluation and might lead a user or clinician to exclude ADHD as primary diagnosis. Negative predictive value (NPV) represents the chance that when an individual patient receives a system negative (EEG-no), that the patient does not have ADHD as primary diagnosis (ADHD-no). When analyzing per the intended use, NPV includes reference to the presence or absence of complications that may be of concern to an ADHD evaluation and might lead to exclusion of ADHD from primary diagnosis.
• FIGS. 7-10 illustrate relative diagnostic accuracies and comparisons for various sample populations evaluated using a system, such as100 inFIG. 1, in accordance with an embodiment of the invention. Results for system diagnostic accuracy per the intended use are listed inFIG. 7. Overall diagnostic accuracy for the adolescents and children tested is about 87%. As shown inFIG. 7, groups of adolescents and children were tested, and statistics for sensitivity, specificity, positive predictive value, and negative predictive value were obtained.
• FIG. 8 illustrates example results that show that the performance of a system, such as100 inFIG. 1, is consistent across demographic groups in accordance with an embodiment of the invention. As shown inFIG. 8, demographic groups such as ages 6-11 years vs. 12-17 years, female vs. male, white vs. non-white, and upper/middle class vs. working/lower class were compared, and statistics for overall accuracy were obtained.
• FIG. 9 illustrates example results that show that the presence of other common childhood disorders do not interfere with the performance of the system, such as100 inFIG. 1, in accordance with an embodiment of the invention. As shown inFIG. 9, the presence of different general types of disorders such as anxiety disorder, mood disorder, disruptive disorder, and learning disorder were compared, and statistics for overall accuracy were obtained.
• FIG. 10 illustrates example results that show that the performance of a system, such as100 inFIG. 1, is consistent in different clinics run by users or clinicians from different fields who are serving different patient populations from a variety of communities and regions in accordance with an embodiment of the invention. As shown inFIG. 10, different user or clinician types such as psychiatry, pediatrics, and psychology; different sectors such as private vs. academic; different regions such as South, West, Northeast, Midwest were compared, and statistics for overall accuracy were obtained.
• In the intended use, certain embodiments such as thesystem100 inFIG. 1 can essentially separate an ADHD group into two subgroups: (1) ADHD-yes/EEG-yes (receiving confirmatory support, “CS”), and (2) ADHD-yes/EEG-no (receiving a recommendation for further testing, “FT). For the certain embodiments such as the system to be of clinical value, the CS (ADHD patients with “EEG-yes”) and FT (ADHD patients with “EEG-no”) subgroups should have statistically significant differences in clinical characteristics that are meaningful to the clinician's evaluation of ADHD. Thus, differences in clinical characteristics of ADHD patients with “EEG-no” should support that the result of further testing for this subgroup would be of benefit to the clinical evaluation.
• In testing of certain system embodiments of the invention, for example, the system shown as100 inFIG. 1, various odds ratio (OR) plots inFIGS. 11-20 show that the system can separate ADHD patients into two subgroups with clinically meaningful differences. All of the OR results are statistically significant (statistical significance was supported when the 95% confidence interval did not cross 1 on the plot.) The differences are clinically meaningful because the evaluated conditions would be of concern to an ADHD evaluation and/or could lead a user or clinician to exclude ADHD from primary diagnosis. Therefore these results substantiate that a system result of further testing, in accordance with an embodiment of the invention, would be of benefit to the clinical evaluation.
• FIGS. 11-20 illustrate various statistical clinical support in the form of odds ratio plots for an example implementation of an embodiment of the invention.
• FIG. 11 shows that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was about twice as likely (by odds) to have a psychiatric disorder that could lead to ADHD exclusion from primary diagnosis in accordance with an embodiment of the invention. Note that inFIG. 11, psychiatric disorders include, but are not limited to, pervasive developmental disorders, psychotic disorders, and bipolar disorders. Disorders also include those caused by a stressing event, namely post-traumatic stress disorder and adjustment disorder.
• FIG. 12 shows that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was approximately over 9 times more likely to have a medical or neurological condition that could simulate ADHD in accordance with an embodiment of the invention. Note that in FIG.12, medical/neurological conditions that may simulate ADHD include, but are not limited to, head injury with ongoing problems, sensory integration dysfunction, auditory processing disorder, substance abuse, tobacco exposure, anemia, headaches affecting attention, congenital encephalopathy, cerebral palsy, mild mental retardation, neuromaturational delays/soft signs, influence of asthma medications).
• FIG. 13 shows that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was approximately over twice as likely to have uncorrected vision or hearing problems that could also simulate ADHD in accordance with an embodiment of the invention.
• FIGS. 14 and 15 show that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was more likely to have had a history of problems with ADHD medications; i.e. approximately over 8 times more likely to have experienced no improvement with ADHD medications, and almost 4 times more likely to have experienced adverse events in accordance with an embodiment of the invention. Adverse events with medications were of significant concern and include cardiac problems, tics, depression, anxiety, compulsiveness, insomnia, weight loss, and headaches. Note that adverse events can include, but not limited to, cardiac problems, tics, insomnia, weight loss, depression, compulsiveness, irritability, anxiety, anger, withdrawal, lethargy, nausea, and headaches.
• FIGS. 16 and 17 show that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was approximately over 4 times more likely to have presented with anger as a primary concern and twice as likely to have aggression as a primary concern in accordance with an embodiment of the invention. Note inFIG. 16 these patients presented with anger as a primary concern and/or had experienced anger arising with use of ADHD medications. Note inFIG. 17 these patients presented with aggression as a primary concern and/or had experienced aggression arising with use of ADHD medications. Many were diagnosed with probable to definite conduct disorder (CD) and/or oppositional defiant disorder (ODD).
• FIGS. 18 and 19 show that the ADHD-yes/EEG-no subgroup (NEBA™: further testing) was approximately over 4 times more likely to be doing well academically and intellectually, and approximately over 7 times more likely to express dissatisfaction with a previous or current diagnosis of ADHD in accordance with an embodiment of the invention. These outcomes were consistent with the initial “gut” response of the user or clinician to the patient, in that this subgroup was about 60% less likely to have given the user or clinician an initial impression of ADHD after the clinical interview (FIG. 20). Note inFIG. 19 some patients presented because further evaluation wanted after previous ADHD diagnosis, or because evaluation sought for other disorders not ADHD. Some patients had general dissatisfaction with ADHD medications. In some, parents and teachers both ruled out ADHD (all scale scores <80th percentile). These results support that patients in this subgroup would be agreeable to the system result of recommendation for further testing.
• InFIG. 20, note the initial impression recorded after user or clinician's unstructured interview of patient and parent (with blinding to EEG).
• The OR results inFIGS. 11-20 show that there are statistically significant and clinically meaningful differences in ADHD patients with “EEG-no” (NEBA™: further testing, “FT”) vs. ADHD patients with “EEG-yes” (NEBA™: confirmatory support, “CS”). ADHD patients with a system result of further testing are more likely to have conditions that would be of concern to an ADHD evaluation and/or could lead a clinician to exclude ADHD from primary diagnosis. If a user or clinician were to follow the system recommendation for further testing in accordance with an embodiment of the invention, he or she would likely find conditions that would be of concern to the ADHD evaluation. As such, the OR analyses support that the system result of further testing would be of benefit to the clinical evaluation.
• It should be noted in the odds ratio (OR) comparisons, that the converse of the results for the “ADHD-yes/EEG-no” subgroup (NEBA™: further testing) is true for the “ADHD-yes/EEG-yes” subgroup (NEBA™: confirmatory support). In other words, ADHD patients with “EEG-yes” (NEBA™: confirmatory support) were less likely to have the complications that would warrant further testing. For instance, the “ADHD-yes/EEG-yes” subgroup was less likely to have had a history of problems with ADHD medications, less likely to be doing well academically and intellectually, and less likely to have expressed dissatisfaction with a previous or current ADHD diagnosis. The “ADHD-yes/EEG-yes” subgroup was less likely to have anger or aggression issues, uncorrected vision or hearing problems, a medical or neurological condition that could simulate ADHD, or a psychiatric disorder that could lead to ADHD exclusion from primary diagnosis. Further, when evaluating an individual of the “ADHD-yes/EEG-yes” subgroup (NEBA™: confirmatory support), the user or clinician would be more likely to have an initial impression of ADHD after the clinical interview. All of these OR results were statistically significant.
• The results of analyses support that certain embodiments of the invention, such as use of thesystem100 inFIG. 1, do succeed within the intended use. In summary, the system measure (e.g., EEG theta/beta ratio) can be reliably determined in children and adolescents who present to clinics with attention and behavior concerns (ICC=0.83 to 0.88). An ADHD patient with a system result of confirmatory support is likely to have ADHD as primary diagnosis (positive predictive value=94%). Confirmatory support by a biological assessment aid could be potentially beneficial in (1) helping to reduce the perceived stigma associated with ADHD and (2) helping the patient and parent to feel more comfortable with the clinician's diagnosis and treatment, which may in turn lead to improved compliance with the user or clinician's management of the disorder. An ADHD patient with a system result of a recommendation for further testing is likely to have complicating conditions that may be of concern to an ADHD evaluation and might lead the user or clinician to exclude ADHD from primary diagnosis (negative predictive value=80%; 9 significant OR results for complicating conditions). OR results showed that an ADHD patient with a system result of a recommendation for further testing was more likely to have the following complicating conditions that may be of concern to an ADHD evaluation and might lead a clinician to exclude ADHD from primary diagnosis: a psychiatric disorder that could lead to ADHD exclusion (OR=2), a medical or neurological condition that could simulate ADHD (OR=10), an uncorrected vision or hearing problem which could simulate ADHD (OR=2), history of adverse events (OR=9) or no improvement with ADHD medications (OR=4), primary concern of aggression (OR=2) or anger (OR=4), doing well academically and intellectually (OR=4), and dissatisfaction with a previous or current ADHD diagnosis (OR=8).
• While the invention has been described in connection with what is presently considered to be the most practical of various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
• This written description uses examples to disclose embodiments of the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of embodiments of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.

Claims (20)

1. A system for identifying a subgroup of attention deficit hyperactivity disorder (ADHD) patients at higher risk for complicating conditions, the system comprising:

a data collection module operable to:

receive, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis; and

obtain EEG data for each of the plurality of patients;

a clinical/diagnostic module operable to:

based at least in part on the EEG data, determine an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation; and

a subgroup identification module operable to:

correlate the indicator with the user's or clinician's evaluation data; and

based at least in part on the correlation, determine at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.

2. The system ofclaim 1, wherein determining at least one subgroup of the plurality of patients comprises determining a first subgroup receiving positive support, and a second subgroup receiving a recommendation for further testing.

3. The system ofclaim 2, wherein the at least one subgroup comprises a subgroup receiving a recommendation for further testing, and the subgroup identification module is further operable to:

obtain further clinical data to determine whether one or more complicating conditions are present which could exclude ADHD as the primary diagnosis.

4. The system ofclaim 1, wherein the subgroup identification module is further operable to:

provide a graded recommendation for any patients receiving support for further testing for complicating conditions before proceeding with diagnosis of ADHD.

5. The system ofclaim 1, wherein the EEG data comprises at least one theta/beta ratio calculation, or a ratio computed by dividing average percent power in the theta frequency band by average percent power in the beta frequency band.

6. The system ofclaim 1, wherein the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides positive support for diagnosis of ADHD, and the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within a predefined age range within the at least one subgroup.

7. The system ofclaim 1, further comprising:

a report module operable to:

output an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.

8. A computer program product, comprising a computer readable medium having computer readable code adapted to be executed to implement a method for identifying a subgroup of attention deficit hyperactivity disorder (ADHD) patients at higher risk for complicating conditions, the method comprising:

providing a system, wherein the system comprises a plurality of software modules, wherein the plurality of software modules comprises a data collection module, a clinical/diagnostic module, and a subgroup identification module;

receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis;

obtaining, via the data collection module, EEG data for each of the plurality of patients;

based at least in part on the EEG data, determining an indicator of ADHD via the clinical/diagnostic module, wherein the indicator supports a positive or negative ADHD evaluation;

correlating the indicator with the user's or clinician's evaluation data; and

based at least in part on the correlation, determining, via the subgroup identification module, at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support of further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.

9. The computer program product ofclaim 8, wherein determining at least one subgroup of the plurality of patients comprises determining a first subgroup receiving positive support, and a second subgroup receiving a recommendation for further testing.

10. The computer program product ofclaim 9, wherein the at least one subgroup comprises a subgroup receiving a recommendation for further testing, and the method further comprises:

obtaining further clinical data to determine whether one or more complicating conditions are present which could exclude ADHD as the primary diagnosis.

11. The computer program product ofclaim 8, wherein the method further comprises:

providing, via the subgroup identification module, a graded recommendation for any patients receiving support for further testing for complicating conditions before proceeding with diagnosis of ADHD.

12. The computer program product ofclaim 8, wherein the EEG data comprises at least one theta/beta ratio calculation, or a ratio computed by dividing average percent power in the theta frequency band by average percent power in the beta frequency band.

13. The computer program product ofclaim 8, wherein the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides positive support for diagnosis of ADHD, the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within a predefined age range within the at least one subgroup.

14. The computer program product ofclaim 8, wherein the method further comprises:

outputting, via a report module, an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.

15. A method for identifying a subgroup of attention deficit hyperactivity disorder (ADHD) patients at risk for complicating conditions, the method comprising:

receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis;

obtaining EEG data for each of the plurality of patients;

based at least in part on the EEG data, determining an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation;

correlating the indicator with the user's or clinician's evaluation data; and

based at least in part on the correlation, determining at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.

16. The method ofclaim 15, wherein the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides positive support for diagnosis of ADHD, the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a suggestion for further testing for complicating conditions before proceeding with diagnosis of ADHD, and the correlation of EEG data comprising a theta/beta ratio calculation within a predefined range provides a strong recommendation for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within a predefined age range within the at least one subgroup.

17. The method ofclaim 16, wherein the at least one subgroup comprises a subgroup receiving a recommendation for further testing, and the method further comprises:

obtaining further clinical data to determine whether one or more complicating conditions are present which could exclude ADHD as the primary diagnosis.

18. The method ofclaim 16, further comprising:

providing a graded recommendation for any patients receiving support for further testing for complicating conditions before proceeding with diagnosis of ADHD.

19. The method ofclaim 16, wherein the EEG data comprises either at least one theta/beta ratio calculation, or a ratio computed by dividing average percent power in the theta frequency band by average percent power in the beta frequency band.

20. The method ofclaim 16, further comprising:

outputting an indication for at least one patient within the at least one subgroup, wherein the indication corresponds to whether the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in the at least one patient.

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